Systems for detecting and analyzing target patterns in digital imagery have a wide variety of uses. Such systems can be used to detect geographical objects, military targets or weather patterns from satellite images. Radar or sonar shadows of airplanes, ships, submarines and schools of fish can also be detected and analyzed. Much effort has been expended to develop systems for detecting and analyzing anatomical regions in radiological images. For example, systems for analyzing computed tomography (CT) images are used for the computer-aided detection (CAD) of cancerous regions in human lungs.
One of the more difficult tasks of detecting patterns in medical images involves detecting cancerous mass lesions and micro-calcifications in X-ray images of breasts (also called mammograms). Early detection of these cancerous regions in the breast increases the chance of survival of women with breast cancer. The X-ray images are digitized, and the pixel data is analyzed. Detecting cancerous regions in breasts is made more difficult, however, by the similar appearance of pixels associated with benign and malignant lesions and micro-calcifications.
Systems for computer-assisted interpretation of mammograms are now widely used to assist in the early detection of breast cancer. Such systems include ImageChecker by R2 Technologies of Sunnyvale, Calif.; Second Look by CADx Systems of Beavercreek, Ohio and MammoReader by iCAD of Hudson, N.H. These systems are designed to provide very high detection rates of cancerous regions at the expense of “detecting” a significant number of regions that are not cancerous. As the probability threshold of missing a cancerous region is lowered, the rate of incorrectly designating cancerous regions increases. Thus, although current systems have achieved a high degree of sensitivity, there remains a tradeoff between the probability threshold for detected objects and the false positive detection rate. Systems employing computer-aided detection (CAD) of early breast cancer can, therefore, be improved by decreasing the false positive detection rate while maintaining the detection of nearly all cancerous regions.
Current CAD schemes for analyzing mammograms to detect breast cancer involve rules-based selection of abnormal regions. The rules are based on pixel filtering and thresholding and the dimensions and orientation of the target region. For example, pixel data from a mammogram is filtered according to brightness or intensity, and pixels with a similar brightness are associated together as an object. A gradient histogram is used to indicate the statistical distribution of brightness among all pixels of the mammogram. The histogram is then used to define thresholds for the brightness of pixels that are associated together. In addition to filtering and thresholding, the distance of one pixel from another pixel may be used to determine whether pixels are associated together. For example, the spatial orientation and the ratio of the dimensions of an area of brightness may be used to determine whether the area is cancerous. Once the CAD scheme has been developed, however, the process of detecting abnormal regions is static. Although the threshold and filtering variables and the target dimensions can be adjusted, the process in which the rules are applied does not change once the CAD scheme begins analyzing a particular digital image.
An improved CAD scheme is sought for locating specified image structures in a digital image that decreases the false positive detection rate while detecting substantially all of the target objects in the digital image. Such an improved CAD scheme is desired in which the process itself adapts to the characteristics of the digital image in which the target objects are located. Moreover, such an improved CAD scheme would detect an object in a digital image by employing processes in addition to associating pixels with an object based on filtering and thresholding pixels and on the dimensions of the object.